docs: def _ErrorOfDifferences(self, cov, warning_cutoff=1.0e-10):
          """
          inputs:
          cov is the covariance matrix of A
  
          returns the statistical error matrix of A_i - A_j
          """
  
          diag = np.matrix(cov.diagonal())
          d2 = diag + diag.transpose() - 2 * cov
  
          # Cast warning_cutoff to compare a negative number
          cutoff = -abs(warning_cutoff)
  
          # check for any numbers below zero.
          if np.any(d2 < 0.0):
              if np.any(d2 < cutoff):
                  print("A squared uncertainty is negative. Largest Magnitude = {0:f}".format(
                      abs(np.min(d2[d2 < cutoff]))))
              else:
                  d2[np.logical_and(0 > d2, d2 > cutoff)] = 0.0
          return np.sqrt(np.array(d2))